Upload app.py

app.py

@@ -0,0 +1,144 @@
+# import streamlit as st
+# from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+# import nltk
+# import math
+# import torch
+
+# model_name = "afnanmmir/t5-base-abstract-to-plain-language-1"
+# # model_name = "afnanmmir/t5-base-axriv-to-abstract-3"
+# max_input_length = 1024
+# max_output_length = 256
+
+# st.header("Generate summaries")
+
+# st_model_load = st.text('Loading summary generator model...')
+
+# # @st.cache(allow_output_mutation=True)
+# @st.cache_data
+# def load_model():
+#     print("Loading model...")
+#     tokenizer = AutoTokenizer.from_pretrained(model_name)
+#     model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+#     nltk.download('punkt')
+#     print("Model loaded!")
+#     return tokenizer, model
+
+# tokenizer, model = load_model()
+# st.success('Model loaded!')
+# st_model_load.text("")
+
+# with st.sidebar:
+#     # st.header("Model parameters")
+#     # if 'num_titles' not in st.session_state:
+#     #     st.session_state.num_titles = 5
+#     # def on_change_num_titles():
+#     #     st.session_state.num_titles = num_titles
+#     # num_titles = st.slider("Number of titles to generate", min_value=1, max_value=10, value=1, step=1, on_change=on_change_num_titles)
+#     # if 'temperature' not in st.session_state:
+#     #     st.session_state.temperature = 0.7
+#     # def on_change_temperatures():
+#     #     st.session_state.temperature = temperature
+#     # temperature = st.slider("Temperature", min_value=0.1, max_value=1.5, value=0.6, step=0.05, on_change=on_change_temperatures)
+#     # st.markdown("_High temperature means that results are more random_")
+
+# if 'text' not in st.session_state:
+#     st.session_state.text = ""
+# st_text_area = st.text_area('Text to generate the summary for', value=st.session_state.text, height=500)
+
+# def generate_summary():
+#     st.session_state.text = st_text_area
+
+#     # tokenize text
+#     inputs = ["summarize: " + st_text_area]
+#     # print(inputs)
+#     inputs = tokenizer(inputs, return_tensors="pt", max_length=max_input_length, truncation=True)
+#     # print("Tokenized inputs: ")
+#     # print(inputs)
+    
+#     outputs = model.generate(**inputs, do_sample=True, max_length=max_output_length, early_stopping=True, num_beams=8, length_penalty=2.0, no_repeat_ngram_size=2, min_length=64)
+#     # print("outputs", outputs)
+#     decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+#     # print("Decoded_outputs", decoded_outputs)
+#     predicted_summaries = nltk.sent_tokenize(decoded_outputs.strip())
+#     # print("Predicted summaries", predicted_summaries)
+
+#     # decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+#     # predicted_summaries = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]
+
+#     st.session_state.summaries = predicted_summaries
+
+# # generate title button
+# st_generate_button = st.button('Generate summary', on_click=generate_summary)
+
+# # title generation labels
+# if 'summaries' not in st.session_state:
+#     st.session_state.summaries = []
+
+# if len(st.session_state.summaries) > 0:
+#     # print("In summaries if")
+#     with st.container():
+#         st.subheader("Generated summaries")
+#         for summary in st.session_state.summaries:
+#             st.markdown("__" + summary + "__")
+
+
+# -------------------------------
+
+import streamlit as st
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import nltk
+import math
+import torch
+
+model_name = "afnanmmir/t5-base-abstract-to-plain-language-1"
+max_input_length = 1024
+max_output_length = 256
+min_output_length = 64
+
+st.header("Generate summaries for articles")
+
+st_model_load = st.text('Loading summary generator model...')
+
+@st.cache(allow_output_mutation=True)
+def load_model():
+    print("Loading model...")
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+    nltk.download('punkt')
+    print("Model loaded!")
+    return tokenizer, model
+
+tokenizer, model = load_model()
+st.success('Model loaded!')
+st_model_load.text("")
+
+if 'text' not in st.session_state:
+    st.session_state.text = ""
+st_text_area = st.text_area('Text to generate the summary for', value=st.session_state.text, height=500)
+
+def generate_summary():
+    st.session_state.text = st_text_area
+
+    # tokenize text
+    inputs = ["summarize: " + st_text_area]
+    inputs = tokenizer(inputs, return_tensors="pt", max_length=max_input_length, truncation=True)
+
+    # compute predictions
+    outputs = model.generate(**inputs, do_sample=True, max_length=max_output_length, early_stopping=True, num_beams=8, length_penalty=2.0, no_repeat_ngram_size=2, min_length=min_output_length)
+    decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
+    predicted_summaries = [nltk.sent_tokenize(decoded_output.strip())[0] for decoded_output in decoded_outputs]
+
+    st.session_state.summaries = predicted_summaries
+
+# generate summary button
+st_generate_button = st.button('Generate summary', on_click=generate_summary)
+
+# summary generation labels
+if 'summaries' not in st.session_state:
+    st.session_state.summaries = []
+
+if len(st.session_state.summaries) > 0:
+    with st.container():
+        st.subheader("Generated summaries")
+        for summary in st.session_state.summaries:
+            st.markdown("__" + summary + "__")